class_docstr: The Speech Commands dataset for keyword detection.
docstr: Yields examples.
docstr_tok: ["Yields","examples","."]
code: def _generate_examples(self, archive, file_list):
      """Yields examples."""
      for path, file_obj in archive:
          if file_list is not None and path not in file_list:
              continue
          relpath, wavname = os.path.split(path)
          _, word = os.path.split(relpath)
          example_id = "{}_{}".format(word, wavname)
          if word in WORDS:
              label = word
          elif word == SILENCE or word == BACKGROUND_NOISE:
              # The main tar file already contains all of the test files, except for
              # the silence ones. In fact it does not contain silence files at all.
              # So for the test set we take the silence files from the test tar file,
              # while for train and validation we build them from the
              # _background_noise_ folder.
              label = SILENCE
          else:
              # Note that in the train and validation there are a lot more _unknown_
              # labels than any of the other ones.
              label = UNKNOWN
  
          if word == BACKGROUND_NOISE:
              # Special handling of background noise. We need to cut these files to
              # many small files with 1 seconds length, and transform it to silence.
              audio_samples = np.array(
                  lazy_imports_lib.lazy_imports.pydub.AudioSegment.from_file(
                      file_obj, format="wav"
                  ).get_array_of_samples()
              )
  
              for start in range(
                  0, len(audio_samples) - SAMPLE_RATE, SAMPLE_RATE // 2
              ):
                  audio_segment = audio_samples[
                      start : start + SAMPLE_RATE
                  ]
                  cur_id = "{}_{}".format(example_id, start)
                  example = {"audio": audio_segment, "label": label}
                  yield cur_id, example
          else:
              try:
                  example = {
                      "audio": np.array(
                          lazy_imports_lib.lazy_imports.pydub.AudioSegment.from_file(
                              file_obj, format="wav"
                          ).get_array_of_samples()
                      ),
                      "label": label,
                  }
                  yield example_id, example
              except (
                  lazy_imports_lib.lazy_imports.pydub.exceptions.CouldntDecodeError
              ):
                  pass
  
file_path: datasets/tensorflow_datasets/datasets/speech_commands/speech_commands_dataset_builder.py
imports: import os
  import numpy
  from tensorflow_datasets.core import lazy_imports_lib
  import tensorflow_datasets.public_api
label_desc: Use image node_id 3 for calling the Builder obj's underlying member method code with example usage: obj._generate_examples(archive, file_list) without return types